Wearable sensors show potential for T1D sensing with informative parameters

Commercially available, wearable technology can improve its sensing ability of physiological signals in the management of type 1 diabetes (T1D) with additional, informative biomarkers, which can be observed noninvasively and continuously, suggests a recent study.

“For the successful adoption of this technology in healthcare in general, and T1D in particular, several challenges still need to be resolved, such as issues related to motion artifacts and noise removal, accurate extraction of the features of interest, and development of decision algorithms for improved and safe disease management,” the researchers said.

A thorough review of devices in the market led to the identification of physiological parameters, which can be monitored using wearable sensors available in 2020. The researchers then performed a literature survey, via PubMed and Scopus databases, using search terms related to T1D combined with the identified parameters.

Of the 626 articles found, 77 (12.3 percent) met the eligibility criteria and were analysed based on the following axes: the reported relations between the parameters and T1D, which were found by comparing T1D patients and healthy control participants, and the potential areas for T1D enhancement through further analysis of the observed associations in studies working within T1D cohorts.

Physiological parameters related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature had the potential for monitoring by using noninvasive wearable devices in T1D patients. [J Med Internet Res 2022;24:e28901]

Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, were found to be necessary in diagnosing and monitoring cardiac autonomic neuropathy, as well as in predicting and detecting hypoglycaemia.

Furthermore, all physiological parameters were associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with a capacity for early risk prediction.

“However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices,” the researchers said.

The few studies that used wearable sensors mostly focused on the detection of hypoglycaemia, glucose prediction, or improvement of continuous glucose monitoring measurement accuracy. [Sleep Sci 2018;11:137-140; Diabet Care 2019;42:689-692; Sensors (Basel) 2017;17(3); Sensors (Basel) 2019;19(17); https://doi.org/10.1109/embc.2019.8856940]

Moreover, most of the studies included in the survey employed conventional statistical analysis methods to examine the existence of associations in their data. Some studies also used machine learning to recognize hypoglycaemia and near-future glucose prediction. [ISA Trans 2016;64:440-446; Soft Comput 201525;21:543-553; J Diabetes Sci Technol 2019;13:919-927; Symmetry 2019;11:1164]

This survey was limited by the search based on a list of wearable-enabled biomarkers, which might not be exhaustive, and the focus only on noninvasive, wearable sensors.

“Current research efforts are working toward advanced algorithmic solutions for the efficient processing of massive amounts of data produced by wearable sensors,” the researchers said. “Their promising results can pave the way for similar endeavours for T1D.”